# @FileName  : qwenApi.py
# @Time      : 2025/3/19 19:10
# @Author    : LuZhaoHui
# @Software  : PyCharm

import torch
from modelscope import snapshot_download
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

# default: Load the model on the available device(s)
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
# "Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
# )
model_dir = "./qwen2.5_vl_7B" #snapshot_download("Qwen/Qwen2.5-VL-3B-Instruct")
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_dir,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(model_dir,max_pixels = 1280*28*28)

messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    #"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
                    "image": "2.jpeg",
                    #"image": "E:/my_data/temp_img/20250222200343.jpg"
                 },
                {"type": "text", "text": "描述这张图。"},
            ],
        }
    ]

# Preparation for inference
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(output_text)

